基于时序MODIS NDVI的黑河流域土地覆盖分类研究

Land Cover Classification Based on Time-series MODIS NDVI Data in Heihe River Basin

归一化植被指数(NDVI)是植被生长状态及植被覆盖度的最佳指示因子,其时序数据也已成为基于生物气候特征开展大区域植被和土地覆盖分类的基本手段。基于时序NDVI数据的土地覆盖分类,即通过提取NDVI时间信号所包含的植被生物学参数,构建起一个包含植被生物学信息的分类特征空间。利用2006年重建得到的MODIS NDVI 16天合成时间序列数据,并结合1 km分辨率的DEM数据、野外实地调查资料等辅助数据,综合分析了不同土地覆盖类型对应的时序NDVI谱线及其第一、二谐波的特征阈值,建立决策树对黑河流域的土地覆盖开展分类研究。结果表明,基于时序MODIS NDVI谱线特征的决策树分类精度为78%,Kappa系数为0.74。利用1 km时序MODIS NDVI时间序列获得较为准确的黑河流域土地覆盖类型是可行的。

Temporal changes in the normalized difference vegetation index （NDVI） have been widely used in vegetation mapping due to the usefulness of NDVI datasets in distinguishing characteristic seasonal differences in the phenology of greenness of vegetation cover. The Time-series Moderate Resolution Imaging Spectroradiometer （ MODIS） NDVI datasets hold considerable promise for large-area land cover classification given their global coverage, intermediate spatial resolution, high temporal resolution （16-day composite period）, and cost-free status. This study focused on generating effective classification features from multi-temporal MODIS NDVI datasets to improve classification accuracy in the Heihe River Basin. Two types of features were derived from reconstructed multi-temporal MODIS NDVI datasets. The first are the basic parameters including the annual maximum NDVI, the mean NDVI during the growing season, the inter-annual variability of NDVI and the annual mean NDVI. The second are the amplitude and phase information of the first and second harmonic components derived from the shape of the time-series NDVI profile. Additionally, DEM with l km resolution has also been used to simplify the current scheme. According to the validated results with 469 ground truth survey samples, the overall land cover classification accuracy using the decision tree was 78% and a Kappa coefficient is 0.74. The results support using decision tree classification based on 1 km MODIS NDVI temporal and derived parameters to provide an up-to-date land cover mapping. However, the current decision tree does not work well in the downstream of the Heihe River Basin since the NDVI of non-vegetation types can not represent the temporal feature of these types. Thus, new effort is necessary in the future in order to imnrove the overall performance on this issue.